The metrics we considered for privacy, QoS and statistical utility are fairly standard in the literature.
A typical information theoretic quantification of privacy is done using mutual information (MI) (a.k.a. conditional entropy) between the original and the noisy data exploiting the compatible relationship between MI and DP [1]}. The approach to measure privacy with MI is widespread in the literature: to gauge anonymity [2]}, [3]}, to estimate privacy in machine learning complementing typical loss function used for training, i.e., cross entropy [4]}, [5]}, [6]}, [7]}, to assess location-privacy [8]}.
